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Weather and Climate Error in a Nested Regional Climate Model: OLR and

Precipitation

NWP Final Project: Fall 2012

Authors: James S. Brownlee, Angie R. Lassman, Robert S. James

Date Submitted: December 07, 2012

Abstract

Global Climate Models (GCMs) are used heavily to conduct research on how the atmosphere will progress into the future. However, the errors within these models can quickly become large due to the amount of complicated processes that occur in the atmosphere on a variety of time scales. An important aspect of this is how quickly does a GCM’s error become indistinguishable from the mean climate error. Once this occurs, it is difficult to get accurate weather forecasts from these models. In order to reduce errors in these models, it is important to analyze many different output quantities from them. For our research, we analyzed a nested regional climate model temporally, ranging from daily to seasonally to yearly, and spatially, focusing on the errors over land and ocean. Our goal for this research is to be able to explain why errors in this model are occurring and what could be done to correct them.

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1) Introduction All atmospheric models are created and run knowing that they will inherently contain

some error in their output, no matter how good the equations or physical parameterizations are,

which increases as the model generates a forecast field becomes farther from its initial state

(Allen et al. 2006). Being able to analyze and understand these errors is a method for

continuously improving atmospheric models. One way in which to know how far a model’s

forecast field has strayed from the truth is to compare it to observations at the time in which the

forecast is valid for, which is the emphasis of our research for this paper.

In this study, we will be analyzing output from a nested regional climate model (NRCM),

focusing on precipitation (both convective and non-convective components) and how it relates to

observed outgoing longwave radiation (OLR). In addition, we will provide some insight of the

various errors found in this model, including how quickly precipitation errors within the model

equal and surpass the mean climate error. The model output is from a version of the Weather

Research and Forecasting model (WRF), which has a horizontal resolution of 36 km, a zonal

domain of 0°W to 360°W, a meridional domain of 30°S to 45°N, and a daily time interval. Our

study will only focus on this domain, which is the coarsest of three within the NRCM. Also, our

research will concentrate on the meridional domain of 30°S to 30°N, and keeping the zonal

domain intact (Fig. 1), to be able to more easily complete an analysis between the northern and

southern hemispheres. The time domain for this simulation was defined between 1 January 1996

and 31 December 2000. Our investigation has been aided by computer software from the Center

for Ocean-Land-Atmosphere Studies called the Grid Analysis and Display System (GrADS),

which allowed us to display the model data.

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First, we will look at a simple error analysis of the modeled output compared with

observation data. This will be done using three time delineations: seasonally, annually, and

overall mean error. The second portion of our research will be a statistical analysis of the model

errors, as well as the seasonal and mean trends in OLR data. Finally, we will discuss the

differences in the convective and non-convective components of the model output, and complete

an analysis of zonal and meridional averages in the model output. Combined, these research

points will help us to understand errors in atmospheric models; such as the one we are examining,

and why they may be occurring.

2) Error Analysis

2.1) Seasonal Error

The first figure created is an error analysis of the WRF model output precipitations and

the observed precipitation data. The model output uses the sum of the convective and non-

convective precipitation while the observed data is the total rain. The time delineation is

seasonal from 1996 to 2000, divided into sections from March to May, June to August,

September to November, and December to February at 30°N to 30°S. The red and blue shading

scale values in Fig. 2 are determined by subtracting the observed precipitation from the model

precipitation. Therefore, the positive values correlate to model overestimation while negative

values are model underestimation.

The pattern of seasonal variances between the Northern and Southern Hemispheres in Fig.

2 leads to the theory that the incoming radiation is indirectly affecting the over or

underestimation of precipitation in the WRF model (Thornton and Running 1999; Richardson

1981). It is obvious in Figs. 2A, D that from December to May the southern hemisphere is

receiving the most incoming solar radiation. This is also the case when focusing on Figs. 2B, C

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except during this time period, the Northern Hemisphere is receiving more incoming solar

radiation. During those time frames, these locations experience a large overestimation in total

precipitation by the NRCM. The fact that convection occurs when the Earth’s surface within a

conditionally unstable or moist atmosphere is warmer than its surroundings which leads to

evaporation and rising motion in the air column. This process promotes the formation of a

convective cloud, such as cumulonimbus or cumulus congestus. The solar radiation ties in with

this process during the diurnal heating of the earth's surface, with more incoming solar radiation

usually correlating to larger amounts of convective precipitation (Winslow and Hunt Jr. 2001).

Also, precipitation is known to be very high near the equator due partially to the influence of the

Intertropical Convergence Zone (ITCZ) (Waliser and Gautier 1993). This is due to the

convergence of the northeast trade winds and southeast trade winds in a low-pressure zone

coupled with the high ability of convection, which then promotes lifting in the air column. The

ITCZ is clearly defined with the NRCM overestimating precipitation on each of the seasonal

subfigures in Fig. 2. Because this region is usually known for its high amount of precipitation, it

is likely that the model has a bias in the dynamic parameters of this region, thus causing the

overestimation (Waliser and Gautier 1993). However, the location of this high precipitation

region or ITCZ does however vary throughout the year. The northward and southward seasonal

movement of this zone can be seen when comparing each subfigure in Fig. 2. One issue that

becomes apparent when analyzing this figure is the failure to depict the seasonal transition of the

ITCZ. The secondary meridional circulation induced by convective momentum transport (CMT)

within the ascending branch of the Hadley circulation is a missing dynamical mechanism that

can cause common failure of general circulation models (GCMs) in simulating seasonal

migration of ITCZ precipitation maximum across the equator (Janowiak et al. 1995; Wu 2003).

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This failure is created from the model bias precipitation peak remaining north of the equator

during November through March.

2.2) Annual Error

Another way to analyze climate and weather errors in models is to examine them from an

annual point of view. Fig. 3 is organized as yearly data from 1996 to 2000. The annual data

shown in this figure is very similar to above explanation of Fig. 2, as it is the error of the model

output (sum of convective and non-convective precipitation) compared with total rain

observations. The purpose of comparing the data by year is to determine if specific

meteorological or oceanographic climatological events occurring in that time period have

affected the models output. With this said, an occurrence would have to be very large for its

impact to show up on an entire years model data. Therefore, eliminating all mesoscale events

that may have occurred during the time period. After some research, it was discovered that one

of the strongest El Niño event ever was recorded in 1997 to 1998 (Chambers et al. 1999;

Williamson et al. 2000). An El Niño is an anomalous ocean warming, occurring about every five

years, which generates a dominant source of climate variability across the globe. The

atmospheric component linked to El Niño is the Southern Oscillation based upon research from

many studies (Rasmusson and Carpenter 1983; McPhaden et al. 1998). This is a fluctuation of

surface air pressure at sea level in the tropical eastern and western Pacific Ocean. Experts believe

that these atmospheric and oceanic temperature anomalies collaborate together forming the

phenomena called El Niño Southern Oscillation (ENSO) (Trenberth 1997). ENSO is likely

related to many unusual climatic events occurring around the globe, such as increase in monsoon

rainfall, drought and an extraordinary number of storms (Rasmusson and Wallace 1983). When

comparing the effects of ENSO to the annual model and observed data characteristic of ENSO

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are apparent in Fig. 2B during the year of 1997. Studies of this event show that heavy monsoons

occurred as an affect of ENSO. While the Southern Oscillation isn’t the only factor influencing

the monsoon seasons in Southeast Asia, there is a close association between ENSO and the

summer monsoon rainfall, based on how precipitation was distributed (Ropelewski and Halpert

1996). Fig. 3B, compared to the rest of the subfigures, shows a high amount of overestimation in

the usually wet region of Southeast Asia. This leads to the idea that the model is positively

biased towards heavier precipitation. Also, the interannual change in the extratropical

wintertime jet stream over the western and central Pacific is highly influenced by ENSO due to

the close relation to the distribution of tropical convection across Indonesia and the tropical

Pacific. The Pacific Ocean Basin and the Indian subcontinent have both shown precipitation

patterns directly related to the ENSO (Ropelewski and Halpert 1987). These variations change

the location of the jet stream, extending it further over the eastern Pacific and towards the

equator. These atmospheric conditions then play a role in enhancing storminess and above-

normal precipitation at lower latitudes of both North and South America. This is apparent again

in Fig. 3B off the coast of South America. Annually organizing the data in Fig. 3 clearly defines

the heavier precipitation regions along the ITCZ by the NRCM overestimation of precipitation.

2.3) Mean Climate Error

The mean climate error of the modeling using the sum of the convective and non-

convective precipitation compared with the total rain observed is shown in Fig. 4. This

comparison is helpful when looking for an overall prominent climatologically error in the model.

For this figure, we used the five-year averages of model estimated and observed precipitation

over the entire time domain of the model from 1 January 1996 to 31 December 2000. The model

overestimation is again shaded in the darker red and model underestimation is shaded blue. This

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image reinforces the idea that the WRF model overly pronounces the relatively wetter regions

along the tropical belt, such as the ITCZ and areas associated with monsoons or large amount of

rain. This also carries over into the dry regions with higher values of underestimation such as

areas of Australia and the desert regions of Africa. This regional model continually shows an

overestimation over water regions, such as the Indian Ocean, Western Pacific, and Eastern

Pacific and along the ITCZ.

3) Statistical Analysis

3.1) Root Mean Squared Error

Statistically analyzing data for errors can be helpful when looking for specific deviations

in data or certain patterns (Wilks 1995, section 7.5.2). For the purposes of our research, we used

the Root Mean Squared Error (RMSE) of the model output (sum of convective and non-

convective precipitation) compared with the observations (total rain). The RMSE is defined for

our purposes as Equation 1, where ym is the model data at time-step “m”, om is the observation

data at time-step “m” and “M” is the total number of time-steps in the data. A perfect forecast

will yield an RMSE value of zero, but the limit of RMSE values is infinite as errors become

larger.

                                                                                                                   𝑅𝑀𝑆𝐸 =(𝑦! − 𝑜!)!!

!!!𝑀

                                                                                                                                     (1)

The RMSE values shown in Fig. 5 were taken at each individual grid point, over the

entire five-year simulation. The results in this figure correlate well with the discussion of Figs. 2,

3, and 4 as the areas of high over or under estimation correlate to areas of high RMSE. The

highest RMSE values are mainly focused in the Indian Ocean, Western Pacific Ocean, and just

of the coast of northwestern South America.

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3.2) Error Growth

Fig. 6 depicts the error growth of precipitation in the model domain. A calculation of

precipitation RMSE for each individual day for the month of January 1996 and the average

climate precipitation root mean squared error over the entire 5-year simulation is shown in Fig. 6.

The purpose of this image is to analyze how soon after model initialization does the precipitation

errors match up to the mean climate error. Using this method, our results show that our NRCM

took approximately seven days the for precipitations errors to catch up to the mean climate error,

calculated over the entire five-year period, and seemed to oscillate around that mean for the

remainder of the month of January. The usage of the model for forecast purposes on any given

day after 8 January 1996 would have yielded very inaccurate results, and thus the error seen in

the climatological mean would be just as good, if not better, than the forecast output from this

NRCM. In order to receive accurate precipitation forecasts and creating forecasts, it would be

necessary to run the model at least every 7 days in order to achieve lower forecast errors than the

climatological average. Since the only available initialization time for our study was 1 January

1996, we are unable to see if this is a continuous error in this model if it was able to be initialized

more often, or if there was another underlying issue causing the RMSE to rise so quickly. To

determine this we would need to be able to initialize this model and numerous times to see if the

error is reoccurring. During this process it would also be necessary to see how it changes based

on the time of initialization, instead of only having output on a daily time scale.

4) Comparison of Precipitation Error to Outgoing Longwave Radiation

4.1) Background on Outgoing Longwave Radiation Techniques

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In this section of the paper, an analysis of the relationship between outgoing longwave

radiation (OLR), cloud cover, and precipitation intensity will be carried out over the domain that

is described in the introduction section. However before getting into the analysis of these results,

some background on this subject area is needed. Since 1974, NOAA’s polar orbiting satellites

have been making estimates of OLR (Hartmann and Recker 1986; Xie and Arkin 1998). This

continual input of OLR data has been used in many different meteorological studies. One very

large and prominent area of research in this field has focused on OLR trends over the tropics.

Since there is little variation in temperature over the tropics, any significant changes in OLR are

generally caused by changes in cloud cover. Since the early 1980s, OLR has been used to

quantitatively estimate precipitation, and many studies have made use this technique; this

technique has proven to be a very accurate way to remotely estimate precipitation (Xie and Arkin

1998; Liebmann 1998). Some examples of how this technique is applied in studies will be shown

in the following examples.

Markin and Meisner (1987) used a Geostationary Operational Environmental Satellite

(GOES) Precipitation Index (GPI) to study the correlation between values of OLR and

precipitation over different regions of the tropics. In their study, they found that low values of

OLR correlated with larger amounts of precipitation (Markin and Meisner 1987; Xie and Arkin

1998). In a 1991 study, Janowiak and Arkin developed an equation, using statistical techniques,

which could estimate precipitation based on values of OLR. The techniques used in these studies

considered the total flux of OLR. Deep convective clouds largely control this flux over the

tropics because there is little variation of temperature over such regions of the planet (Xie and

Arkin 1998). Such techniques cannot be used in the same manner over the middle latitudes

because there are larger changes in temperature; these changes in temperature also regulate the

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OLR flux, and not solely dependent on changes in cloud coverage. In their 1998 study, Xie and

Arkin investigated what the correlations are between global OLR values and precipitation

intensities. In their study, they found a strong relationship between low values of OLR and high

precipitation rates of the tropical regions. Over the middle latitude regions, the cloud coverage-

OLR relationship was not as clearly defined because as stated above, the influence of

temperature changes also controls changes the overall OLR flux.

Trends in OLR can also be used to study the decadal shifts in convection over the tropics.

In their 1997 study, Chu and Wang used statistical methods to study the changes in overall

convection that occurred from 1974-1992. In their study, they found that over the last several

years, OLR values had greatly decreased over the West Central Pacific, over the Indian Ocean,

and over the Bay of Bengal. This was attributed to an overall increase in convective rainfall in

this region, and this increase in rainfall was primarily attributed to a gradual strength in the

monsoon. Over this same period, OLR values increased over Australia, signifying a decrease in

rainfall (Chu and Wang 1997). So, in summary, these studies clearly show that OLR values can

be used to study convective rainfall patterns over the tropical belt. Now that some background on

this subject has been covered, the current analysis of OLR and precipitation over the tropical belt

can be carried out.

4.2) Precipitation Error Correlated to OLR

Fig. 7 shows the overall five-year average of satellite observed OLR values starting on 1

January 1996 and ending on 31 December 2000. Also shown in this figure is the difference

between the five-year average of observed precipitation amounts and the five-year average

model precipitation amounts. The five-year averaged model precipitation is the sum of both

convective and non-convective rainfall. The trend in precipitation in this figure is similar to Figs.

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2, 3, and 4. The NRCM WRF model overestimated rainfall over regions with large amounts of

precipitation, and it underestimated precipitation in dry regions. So, it could be said that the

model is positively biased towards regions with increased rainfall, and negatively biased towards

regions with little rainfall. These positive and negative biases towards regions of high and low

precipitation have been observed in other model studies, particularly for those where a nested

regional climate model was used (Liu et al. 2012; Murthi et al. 2010). So, in summary, the model

overestimates precipitation in regions with high rainfall and the model underestimates

precipitation in regions with little rainfall.

Despite these biases, the models output clearly does show a strong correlation between

high precipitation regions and outgoing longwave radiation. In Fig. 7, the regions with the

highest precipitation contain the lowest values of OLR. In contrast, the regions with little

precipitation have much higher values of OLR. Over the Indian Ocean, the OLR values are quite

low, and in this region the model output depicts higher precipitation. This also occurs along the

ITCZ, over Central Africa, and over northwestern South America. As shown in Figs. 9 and 10,

the model output shows that convective precipitation dominates in these regions. It is likely that

the model is overestimating the amount of convective precipitation over the tropical regions,

especially over the ocean. A more in depth discussion of this potential error will occur later on

this section of the paper. Over Australia and Northern Africa the OLR values are much higher,

and as shown in the model output, there is little precipitation in these regions. In addition to that,

the model output suggests that there is almost no convective precipitation in these drier regions.

Previous research shows that OLR flux within the tropical belt is mostly controlled by

convective clouds, (Xie and Arkin, 1998; Markin and Meisner, 1987) and this trend is clearly

shown in Fig. 6. The OLR is much lower in regions where there is a local maximum in rainfall

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and vice versa. So, despite the models bias towards dry and wet regions, the model output when

plotted with satellite derived OLR data, clearly shows the correlation between convective cloud

cover and OLR values. The regions with lower OLR values correlate with higher amounts of

convective rainfall, and the regions with higher OLR have little precipitation and almost no

convective rainfall.

4.3) Seasonal OLR Analysis

Fig. 8 shows the five-year seasonal average of OLR from 1 January 1996 to 31 December

2000. Each of the five-year OLR averages are taken over three month periods, the first being

March, April, and May (Northern hemisphere spring); the second being June, July, and August

(Northern hemisphere summer); the third being September, October, and November (Northern

hemisphere fall); and the fourth being December, January, and February (Northern hemisphere

winter). Similar to Fig. 7, the regions with low OLR values correlate with higher precipitation

rates (Markin and Meisner, 1987). There are several interesting trends that can be deciphered

within these seasonal precipitation patterns. First, during all four seasons, the OLR data clearly

displays the ITCZ over the tropical belt. In addition to that, during the March, April, and May

time period, the heaviest precipitation occurs over the northern part of South America, over

Central Africa, and over the island chains in the central part of the Western Pacific Ocean. By

June, July, and August, the rainfall area decreases over South America, and it increases greatly

over the central part of the Western Pacific and Northern Indian Oceans. Also, the rainfall

increases over the eastern part of the Central Pacific. During the September, October, and

November time period, rainfall remains heavy over the Indian Ocean and over the West Central

Pacific, and it increases over the western part of South America. Secondly, precipitation remains

significant over Central Africa. Over the December, January, and February time period, rainfall

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increases southward over Central Africa and Central South America; the rainfall belt also shifts

slightly southward over the West Central Pacific moving towards Australia. There are many

global and regional oscillations in the atmosphere and ocean that are responsible for these

seasonal shifts in OLR and rainfall over the tropics. In the next part of this section, some

explanations will show how these seasonal patterns cause the observed seasonal shifts in OLR

and rainfall will be attempted, with the primary focus being on the Central Eastern Pacific,

Central South America, and the Northern Indian Ocean.

The large area of negative OLR values observed over South America during the

December to February time period (Southern hemisphere summer) is primarily driven by the

convergence that occurs along the South Atlantic Convergence Zone (SACZ) and along the

ITCZ (Nogues-Paegle and Mo, 1997; Liebmann et al 1999). This large area of clouds and

precipitation continues through the March to May time period. By the time Southern hemisphere

winter (June to August) comes, the ITCZ has moved northward and the SACZ has weakened,

thus there is noticeable increase in OLR over Central South America due to decreased rainfall

and cloud coverage.

The next area of interest is over the central part of the Eastern Pacific Ocean. Of interest

here is whether or not the large El Nino that occurred at the end of 1997 and at the beginning of

1998 is discernible over the five-year average of seasonal OLR values. From the Northern

hemisphere spring to summer there is a large decrease in OLR values over the central part of the

Eastern Pacific, indicating an increase in sea surface temperatures (SSTs) and deep convection.

From summer to fall, the five-year averaged values of OLR slightly increase, indicating that the

convection has weakened slightly. By winter, the OLR values indicate little to no convection

over this region. In their 2000 study, Wang and Weisberg, analyzed the different changes in

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atmospheric and oceanic anomalies that occurred during the development and decay of the

1997/1998 El Niño event. In their study, they found that values of OLR began to decrease over

the Central Eastern Pacific between July and September of 1997. During the main stage of the El

Niño, which went from December of 1997 to January of 1998, there was large area of negative

OLR values of the central part of the Eastern Pacific, signifying that widespread deep convection

was occurring over the area during that time. This large area of convection persisted into the

February to April of 1998 time period (Wang and Weisberg, 2000). Comparing this pattern to the

five-year average values of OLR in Fig. 7 suggests that any signal from the 1997/1998 El Niño is

not even noticeable over the five-year seasonal averages. OLR values greatly decreased from

July of 1997 to December of 1997 during the developing El Nino event, while over the five-year

average period, OLR values decrease from summer to winter. Again this suggests that the El

Niño signal is not detectable over the five-year average in observed OLR values. Lastly, it

should be noted that it is possible that the strong convective precipitation estimates in the model

output within Figs. 9 and 10 could have been influenced by the 1997/1998 El Niño event,

however such an assumption is at best inconclusive.

Over the Northern Indian Ocean, the Indian Monsoon controls the seasonal variations in

convection. The monsoon is marked by widespread deep convection over the northern Indian

Ocean during the summer period, especially over the Bay of Bengal (Sengupta and Ravichandran,

2001). In addition to that, convection is enhanced over the eastern part of the Northern Indian

Ocean (Gadgil et al. 2003). This pattern is clearly evident in the 5 year averaged OLR values

over the Bay of Bengal. The lowest OLR values occur during the Northern Hemisphere Summer;

this is when convection is most enhanced over the Bay of Bengal region. Clearly, the five-year

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averaged OLR values over the Northern Indian Ocean are largely controlled by the seasonal

Indian Ocean monsoon pattern.

5) Convective and Non-convective Precipitation

Figs. 9 and 10 show the five-year average in the areal extent of convective and non-

convective precipitation over the tropical belt from 1 January 1996 to 31 December 2000.

Previous research has shown that when sampling the real atmosphere, amounts of convective and

non-convective precipitation should be roughly equal (Gamache and Houze, Jr. 1983; Houze, Jr.

and Rappaport 1984). In Fig. 9, the model output shows that there is whole lot more convective

precipitation over the tropics then there is non-convective/stratiform precipitation. In Fig. 10, it is

again clear that the precipitation in the model output is dominated by convective precipitation. It

is likely that the model has greatly overestimated the areal extent of precipitation that is

convective and underestimated the areal extent of precipitation that is stratiform. Studies have

shown that over the tropics, especially in the oceanic regions, rainfall mainly comes from

mesoscale convective cloud systems (Waliser and Graham, 1993; Schumacher and Houze, 2003).

Within in these convective systems there are three main components: deep convective towers at

the front of the cloud system which release large amounts of latent heat, a large area of trailing

stratiform precipitation, and a large cirrus cloud shield near the tropopause (Waliser and Graham,

1993). The structure of these mesoscale convective systems is common to both tropical and non-

tropical mesoscale convective systems (Rutledge et al. 1988; Ely et al. 2008). Based on the

above description of these tropical convective systems, there is clearly both convective and

stratiform precipitation occurring within these systems. However, the stratiform precipitation

covers a much larger area then the convective precipitation that occurs at the front of the cloud

system. This means that when the average of all these systems is summed up, there should be an

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overall larger area of stratiform precipitation over the tropics, it turns out that studies confirm

this. Schumacher and Houze (2003) used the Tropical Rainfall Measuring Mission (TRMM)

Precipitation Radar over the years of 1998-2000 to estimate the amount of convective and non-

convective precipitation over tropical regions. They found that 73% of the areal extent in

precipitation was covered by stratiform rainfall, but it only accounted for 40% of the total rainfall

during the three-year period (Schumacher and Houze, 2003). An older study in 1996, similarly

found that 74% of the precipitation in the tropics was stratiform, and the remaining 26% percent

was convective precipitation (Tokay and Short, 1996). Based off these studies, it is clear that the

model used in this study has underestimated the areal extent of stratiform precipitation. Most of

the model output shows only convective precipitation over the tropics and very little stratiform

precipitation. This means that this model has greatly overestimated the areal of extent of

stratiform precipitation over the tropics, especially over the Central Western North Pacific and

Northern Indian Ocean.

It is possible that this regional model is having a hard time capturing the small-scale

processes that drive these mesoscale convective systems. This model has a horizontal resolution

of 36 km, so its resolution is not high enough to capture the convective scale processes that occur

within the cloud systems. Studies have shown that decreasing the resolution of the regional

model down to a few kilometers or a few hundred meters is the best way to accurately portray

convective systems (Bryan et al. 2003; Lean et al. 2008; Kalnay p.12). Since this model was run

at a horizontal resolution of 36 km, it is likely having trouble in distinguishing the difference

between convective and non-convective precipitation within the mesoscale cloud systems. So

perhaps the coarse resolution of this WRF model is one reason why the model is greatly

underestimating the areal extent of stratiform precipitation in the tropics.

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6) Zonal and Meridional Averages

Fig. 11 shows the zonal (west to east) five-year model output average of convective

precipitation, non-convective precipitation, and observed OLR values from 30°S to 30°N. As

with the other figures in this report, the time goes from 1 January 1996 to 31 December 2000.

This figure clearly shows the correlation between values of OLR and the amount of convection,

which occurs at each zonal average. At between 5°N and 10°N, the convection is at its strongest.

This large maximum five-year zonal average in convection is clearly being caused by the tropical

rain belt/ITCZ. When the convection is at its maximum value, OLR values are at a minimum.

This figure again shows that when there is lots of cloud cover and convection, OLR values are

quite low. OLR values and non-convective precipitation do not appear to be correlated; it is

primarily dominated by convective precipitation. As discussed earlier, this model is likely

overestimating the areal extent of convective precipitation over the tropics.

Fig. 12 displays the meridional (North to South) five-year model output average of

convective precipitation, non-convective precipitation, and observed OLR values starting and

ending at the Prime Meridian. In other words, this figure shows the meridional average across

the entire globe. There are two average maximums in convective activity that occur in this figure.

The first is at roughly 60° W. At this longitude, there is a large amount of equatorial convection

in South America, and this is contributing to the meridional average maximum in Fig. 12. As

expected, at this longitude, values of OLR are at a minimum. The second and much stronger

maximum in convective activity occurs from longitudes 60°E to 160°E. Most of this maximum

in convection is due to the monsoon that occurs over the Northern Indian Ocean and the large

warm pool that occurs over the Central Western Pacific Ocean. So, this figure shows that on

average, the strongest and most persistent convection occurs over the Northern Indian Ocean and

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the Central Western Pacific; other studies have also shown that this region generally has the

largest areal coverage of intense convection over the tropics (Gettelman et al. 2002). Again it

should be noted, that according to this model output, the non-convective precipitation is not well

correlated with values of OLR, the model output is dominated by convective precipitation.

7) Conclusion

Our research shows that the NRCM used has high amounts of convective precipitation

over a large majority of its domain due to convective parameterization within the coarse

resolution of this model. We analyzed how model errors differed between various temporal

scales, and what meteorological and climatological features could have resulted in these errors.

Some of the errors found within the model can be correlated to the observed OLR data over the

same time period. ENSO was found to be a key factor in some of the model errors when looking

at yearly averages from 1996 to 2000, as an El Niño event occurred during 1997 and 1998 and

can be seen in some of the error fields in Fig. 3. When trying to use this model to predict

precipitation amounts into the future, we found that this model is very poor and its errors are

roughly equal to the mean climate error approximately seven days after initialization. In order to

minimize the model’s weather error, it would need to be run at least every seven days for

accurate weather forecasts to be made. The NRCM also has large errors associated with its

convective parameterization scheme, which it relies on heavily being a coarse resolution model.

We created Fig. 9 to show the large discrepancy the model has between amounts of convective

and non-convective precipitation. Similar to precipitation errors, zonal and meridional averages

of convective precipitation, studied in Figs. 11 and 12, were found to also correlate well to

observed OLR values. Areas that observed lower amounts of OLR also experienced a rise in the

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amount of convective precipitation, while areas with higher amounts of OLR experienced a

decrease in the amount of convective precipitation. Overall, we feel that this model is not a great

estimator of the climate throughout its domain and needs to be improved. In addition, its ability

to forecast weather conditions does not seem viable after approximately seven days after

initialization.

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Fig. 1. Zonal domain of the NRCM used in this research. The red box denotes the approximate domain in which our research focuses, from 30°S to 30°N and 0°W to 360°W.

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Fig. 2. Seasonal error of model output (sum of convective and non-convective precipitation) and observations (total rain) between 1996 and 2000 with units of mm day-1. Positive values correlate to model overestimation.

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Fig. 3. Annual error of model output (sum of convective and non-convective precipitation) compared with observations (total rain) between 1996 and 2000 with units of mm day-1. Positive values correlate to model overestimation of precipitation.

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Fig. 4. Mean climate error of model output (sum of convective and non-convective precipitation) compared with observations (total rain) between 1 Jan 1996 and 31 Dec 2000 with units of mm day-1. Positive values correlate to model overestimation.

Fig. 5. Root mean squared error (RMSE) of model output (sum of convective and non-convective precipitation) compared with observations (total rain) between 1 Jan 1996 and 31 Dec 2000.

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Fig. 6. Calculation of precipitation RMSE (sum of convective and non-convective) for each individual day for the month of January 1996 (blue line), and the average climate precipitation RMSE over the entire 5-year simulation (red line).

Fig. 7. Average OLR (shaded, W m-2 day-1) and a difference field of modeled (sum of convective and non-convective precipitation) and observed precipitation (see Figure 3, contoured, mm day-1) over the period of 1 Jan 1996 to 31 Dec 2000. Positive values of the difference field correspond to the solid contours; negative values correspond to dashed contours

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Fig. 8. Seasonal average of OLR (W m-2 day-1) between 1996 and 2000.

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Fig. 9. Model output of (a) convective and (b) non-convective precipitation, averaged over the time period of 1 Jan 1996 to 31 Dec 2000. Filled contours have units of mm day-1.

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Fig. 10. Difference of the average model output of convective and non-convective rain. Positive values (red colors) correlate to a larger convective rain average.

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Fig. 11. Zonal average of (a) convective, (b) non-convective modeled precipitation (units of mm day-1), and (c) OLR (units of W m-2 day-1) for the period of 1 January 1996 to 31 December 2000.

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Fig. 12. Meridional average of (a) modeled precipitation (convective, black line; non-convective, red line), and (b) OLR (units of W m-2 day-1) for the period of 1 January 1996 to 31 December 2000.

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